Informações gerais

Esse banco de dados é de um resultado de um projeto de Ciência cidadã feito em Arraial do Cabo. A fotos recebidas foram utilizadas para identificação individual das tartarugas marinhas através do método de foto-identificação. O software utilizado para as comparações foi o Hotspotter, com intermédio do Internet of Turtles. Os dados podem ser encontrados no Internet of Turtles procurando pelo ID “isabellaferreira”. Para esse trabalho foram extraídos os encontros (avistamento de um único animal em um local e horário específicos) registrados até a data atual.

  • Name0.value: Identidade única para cada indivíduo
  • Occurrence.occurrenceID: Código único para cada encontro
  • Encounter.verbatimLocality: Local de coleta da foto, se NA, então a foto é de Arraial do Cabo sem definição da praia ou ponto de mergulho
  • Encounter.year: Ano da coleta da foto, formato de quatro dígitos
  • Encounter.month: Mês da coleta da foto, formato de dois dígitos
  • Encounter.day: Dia da coleta da foto, formato de dois dígitos.Dia 0 indica fotos sem dia definido.
  • Encounter.behavior: Comportamento do animal no momento do encontro
  • Encounter.genus: Gênero da espécie do encontro
  • Encounter.specificEpithet: Epíteto específico da espécie do encontro
  • Encounter.occurrenceRemarks: Lateral(is) registrada(s) no encontro, podendo ser direita (right), esquerda (left), ou ambas as laterais (both)
  • Encounter.mediaAsset0: Primeira foto do encontro, podendo ser da lateral direita ou esqueda. Obrigatória para registro do encontro.
  • Encounter.mediaAsset1: Segunda foto do encontro, sendo necessária apenas quando o encontro tem registro das duas laterais.

Pacotes usados

Carregando os pacotes

library(readxl)
## Warning: package 'readxl' was built under R version 4.0.5
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.0.5
## Warning: package 'ggplot2' was built under R version 4.0.5
## Warning: package 'tibble' was built under R version 4.0.5
## Warning: package 'tidyr' was built under R version 4.0.5
## Warning: package 'readr' was built under R version 4.0.5
## Warning: package 'purrr' was built under R version 4.0.5
## Warning: package 'dplyr' was built under R version 4.0.5
## Warning: package 'stringr' was built under R version 4.0.5
## Warning: package 'forcats' was built under R version 4.0.5
library(stringr)
library(psych)
## Warning: package 'psych' was built under R version 4.0.5
library(lubridate)
## Warning: package 'lubridate' was built under R version 4.0.5
library(plyr)
## Warning: package 'plyr' was built under R version 4.0.5
library(plotly)
## Warning: package 'plotly' was built under R version 4.0.5

Analisando o total de indivíduos

Abrindo a tabela e selecionando as colunas

originalenctable <- read_excel("encounterSearchResults_export_isabellaferreira.xls") %>%
  select(Name0.value, Occurrence.occurrenceID, Encounter.verbatimLocality,Encounter.year,Encounter.month,Encounter.day, Encounter.behavior,Encounter.genus,Encounter.specificEpithet,Encounter.occurrenceRemarks,Encounter.mediaAsset0,Encounter.mediaAsset1)

Analisando a tabela

lapply(originalenctable, unique)
## $Name0.value
##   [1] "MdT344"     "MdT316"     "MdT6"       "MdT342"     "MdT72"     
##   [6] "MdT343"     "MdT341"     "MdT314"     "MdT345"     "MdT311"    
##  [11] "MdT312"     "MdT310"     "MdT313"     "MdT303"     "MdT304"    
##  [16] "MdT294"     "Bicuda"     "MdT308"     "MdT307"     "MdT295"    
##  [21] "MdT296"     "MdT293"     "MdT292"     "MdT291"     "MdT300"    
##  [26] "MdT290"     "MdT289"     "MdT299"     "MdT288"     "MdT287"    
##  [31] "MdT285"     "MdT286"     "MdT346"     "MdT330"     "MdT326"    
##  [36] "MdT325"     "MdT327"     "MdT328"     "MdT329"     "MdT302"    
##  [41] "MdT320"     "MdT324"     "MdT322"     "MdT323"     "MdT321"    
##  [46] "MdT319"     "MdT261"     "MdT335"     "MdT333"     "MdT78"     
##  [51] "MdT339"     "MdT33"      "MdT340"     "MdT253"     "MdT331"    
##  [56] "MdT334"     "MdT338"     "MdT336"     "MdT348"     "MdT262"    
##  [61] "MdT259"     "MdT337"     "MdT309"     "MdT305"     "MdT306"    
##  [66] "MdT284"     "MdT280"     "MdT317"     "MdT315"     "MdT282"    
##  [71] "MdT283"     "MdT318"     "MdT277"     "MdT276"     "MdT275"    
##  [76] "MdT278"     "MdT271"     "MdT272"     "MdT273"     "MdT274"    
##  [81] "MdT279"     "MdT270"     "MdT269"     "MdT268"     "MdT298"    
##  [86] "MdT266"     "MdT265"     "MdT267"     "MdT264"     "MdT263"    
##  [91] "MdT75"      "Rachel"     "MdT251"     "MdT250"     "MdT24"     
##  [96] "MdT86"      "MdT249"     "MdT252"     "MdT260"     "MdT256"    
## [101] "MdT254"     "MdT257"     "MdT255"     "MdT258"     "MdT206"    
## [106] "MdT243"     "MdT237"     "MdT159"     "MdT105"     "MdT83"     
## [111] "MdT28"      "MdT244"     "MdT126"     "MdT248"     "MdT229"    
## [116] "MdT224"     "MdT100"     "Fafa"       "MdT54"      "MdT221"    
## [121] "MdT222"     "MdT226"     "MdT84"      "MdT236"     "MdT179"    
## [126] "MdT146"     "Margarida"  "Tikinha"    "Dorminhoca" "MdT234"    
## [131] "MdT183"     "MdT150"     "MdT87"      "MdT90"      "MdT246"    
## [136] "MdT141"     "MdT135"     "MdT124"     "MdT68"      "Drika"     
## [141] "MdT136"     "MdT127"     "MdT191"     "MdT155"     "MdT40"     
## [146] "MdT202"     "MdT149"     "MdT162"     "MdT77"      "MdT138"    
## [151] "MdT74"      "MdT82"      "MdT140"     "MdT184"     "MdT185"    
## [156] "MdT247"     "MdT200"     "MdT80"      "Judite"     "MdT134"    
## [161] "MdT347"     "MdT301"     "MdT148"     "MdT115"     "MdT139"    
## [166] "MdT73"      "MdT114"     "MdT192"     "MdT130"     "MdT228"    
## [171] "MdT175"     "MdT76"      "MdT238"     "MdT147"     "MdT142"    
## [176] "MdT132"     "MdT106"     "MdT39"      "MdT89"      "MdT181"    
## [181] "MdT182"     "MdT92"      "MdT217"     "MdT97"      "MdT117"    
## [186] "MdT31"      "MdT52"      "MdT55"      "Josimar"    "MdT133"    
## [191] "MdT194"     "MdT193"     "MdT204"     "MdT203"     "MdT36"     
## [196] "MdT225"     "MdT154"     "MdT165"     "MdT50"      "MdT196"    
## [201] "MdT241"     "MdT239"     "MdT79"      "MdT151"     "MdT164"    
## [206] "MdT161"     "MdT102"     "MdT101"     "MdT93"      "MdT190"    
## [211] "MdT49"      "MdT96"      "MdT110"     "MdT176"     "MdT48"     
## [216] "MdT116"     "MdT240"     "MdT98"      "MdT88"      "MdT111"    
## [221] "MdT137"     "MdT107"     "Brava"      "MdT177"     "MdT157"    
## [226] "MdT156"     "MdT220"     "MdT119"     "MdT47"      "MdT218"    
## [231] "MdT208"     "MdT212"     "MdT143"     "MdT216"     "MdT118"    
## [236] "MdT153"     "MdT213"     "MdT103"     "MdT211"     "MdT209"    
## [241] "MdT219"     "MdT201"     "MdT95"      "MdT35"      "MdT104"    
## [246] "MdT170"     "MdT123"     "MdT46"      "MdT125"     "MdT172"    
## [251] "MdT169"     "MdT71"      "MdT242"     "MdT160"     "MdT158"    
## [256] "MdT199"     "MdT45"      "MdT163"     "MdT43"      "MdT42"     
## [261] "MdT41"      "MdT231"     "MdT109"     "MdT94"      "MdT128"    
## [266] "MdT205"     "MdT232"     "MdT214"     "MdT230"     "MdT112"    
## [271] "MdT227"     "MdT233"     "MdT197"     "MdT129"     "MdT166"    
## [276] "MdT245"     "MdT207"     "MdT38"      "MdT198"     "MdT186"    
## [281] "MdT62"      "MdT37"      "MdT34"      "MdT32"      "MdT187"    
## [286] "MdT30"      "MdT29"      "MdT131"     "MdT180"     "MdT67"     
## [291] "MdT66"      "MdT27"      "MdT26"      "MdT64"      "MdT168"    
## [296] "MdT25"      "MdT63"      "MdT69"      "MdT70"      "MdT61"     
## [301] "MdT22"      "MdT23"      "MdT21"      "Aninha"     "MdT235"    
## [306] "MdT20"      "MdT19"      "MdT18"      "MdT58"      "MdT59"     
## [311] "MdT60"      "MdT17"      "MdT15"      "MdT16"      "MdT13"     
## [316] "MdT14"      "MdT12"      "MdT11"      "MdT57"      "MdT10"     
## [321] "MdT8"       "MdT9"       "MdT7"       "MdT5"       "MdT3"      
## [326] "MdT56"      "MdT4"       "MdT167"     "MdT2"       "MdT1"      
## [331] "MdT53"     
## 
## $Occurrence.occurrenceID
##   [1] "973de39d-ae5e-42ed-88d0-c434b407c08e"
##   [2] "9ef424bc-29e9-4cbf-8694-40e8475860fb"
##   [3] "e271bbfa-5602-444a-9a93-24638faedb88"
##   [4] "351253c5-4502-4de2-9116-64be4880be19"
##   [5] "7ffdc664-33b3-44b3-84e7-80a72577f956"
##   [6] "74f7ef96-c924-466e-adb0-95444c301cfe"
##   [7] "3402c99a-1ca8-43f3-b404-273425b539b0"
##   [8] "ecf6ee74-3a23-4f4c-be1e-ec7d6f5798ef"
##   [9] "0218cd5f-0fee-4ec3-b86f-73f4078f4e0e"
##  [10] "4e0e877c-9205-4e61-826c-5adddd562280"
##  [11] "930843de-ee99-4917-8bf6-53e81a68b0bf"
##  [12] "cba1cd3b-337b-4af6-a466-12c0b5f1df9e"
##  [13] "ddb8d6ad-48a5-4bac-adf2-143bccac97d6"
##  [14] "6e1e8f49-877b-471f-8c3c-939d6d1c5584"
##  [15] "ed790f9c-26bf-4f5d-911e-bfe825fa6b76"
##  [16] "3475a627-73a9-40a3-b6c2-a634e2ca8e5a"
##  [17] "f6e0e172-3b6e-405e-a99f-2d8e94d1c916"
##  [18] "578d9740-246f-402a-8c81-b06c0157623a"
##  [19] "853854a5-3f0a-423e-9e83-f56854006889"
##  [20] "d6274bf0-759e-410c-98e9-739ce325b558"
##  [21] "2b3bc78b-8b35-4820-8e29-cadde81b7c5d"
##  [22] "9c9b8ef7-01fb-4901-9699-ddbe69d52441"
##  [23] "485a0f19-b8da-4a77-9b51-74a415c88339"
##  [24] "ee62b8b0-97b4-417a-84a1-70ccbd3eed5c"
##  [25] "239c7e90-8a30-4671-b986-85803a3de6d0"
##  [26] "76f19270-931b-46bd-a8bb-8a76859787a3"
##  [27] "72248b37-03ef-40cb-bbf7-849bdaca7c0f"
##  [28] "497fa259-ad2f-4458-8b8e-37d1c59923b1"
##  [29] "3db0f325-5e9b-4a5d-9dd0-8296d8226b6e"
##  [30] "f2c398ba-d5f9-441d-9842-ab39b0414106"
##  [31] "bd39585e-eb8b-4fd3-81c8-0b81aadedddf"
##  [32] "54dd65db-5a5e-4e1f-97d9-477c253443d4"
##  [33] "10a43c8d-d344-456b-b5f1-1693fc651d26"
##  [34] "62224628-7681-4166-8327-999229bdd02e"
##  [35] "73deb8a0-be46-4741-aa04-fe2aee2d79e4"
##  [36] "c118013f-ec09-4a96-a180-e87bf3e252c3"
##  [37] "07ced264-39d9-4b60-87d5-0a7266fc3cf1"
##  [38] "4d37bd87-7d78-4e0e-aa7f-343f83ab215d"
##  [39] "549285f8-0c1d-4f25-a042-e0c96e9ba793"
##  [40] "1457fc4d-eacf-4d01-bddc-ad455d13ce66"
##  [41] "66b7752a-9da7-454d-a222-d564926020b4"
##  [42] "b461abcf-6b4f-4f51-9900-e0ef64c85c05"
##  [43] "9b2225ed-44cc-4ff1-8de1-b6a3a887b95d"
##  [44] "ea9e1c60-2e4a-41b9-830d-b7080ba40725"
##  [45] "bd44ee8e-cb99-49aa-9237-7c1fd9ab7c46"
##  [46] "b53b58c3-be01-4554-8f98-219440bb8bb7"
##  [47] "39e926d9-29fe-4c92-8222-caac4ee4b075"
##  [48] "6d5b3e67-1ada-41c9-a401-21ad61ff1b03"
##  [49] "7d722e89-d9ac-49cd-8b15-23202ba79a2b"
##  [50] "188a512c-45ab-4280-a7e5-f8a1a6a5b0ab"
##  [51] "c22c7d9d-8f98-4fa5-910e-9cc1db240042"
##  [52] "595c88ae-a00c-4c8a-907f-fd84e6a3f75e"
##  [53] "660f911c-c9a9-410b-b94f-0352ea816ffd"
##  [54] "76b64337-1695-4834-a947-9d165ddf9188"
##  [55] "76e33fbe-e990-4b38-8a71-f96781408019"
##  [56] "7b29ddc3-80b9-463d-9473-b4062508f6f2"
##  [57] "4dd55dad-dba1-4b72-a616-aab131f4c8ee"
##  [58] "a13339eb-4a1f-4ac4-9fa3-54e98e5c24f1"
##  [59] "a671973a-eb6c-43b8-9f8c-e79017aae71d"
##  [60] "a243d263-a1ad-4fb1-a869-abf1bb8dd2b9"
##  [61] "826d424b-10b5-4de9-9e79-6347cf777962"
##  [62] "a67cc0ff-b75a-4abe-86c4-1e8376fabe77"
##  [63] "c9388721-d0ac-4448-929f-211ab9221d6d"
##  [64] "6666d2ea-023b-407d-a2af-2c7f65887aea"
##  [65] "14aeb1a1-f127-42b2-9420-bc2ad51adadc"
##  [66] "4b23bd80-2b61-47bd-8f9f-d119c9947592"
##  [67] "715271aa-c177-4fec-882f-5b97508e1685"
##  [68] "5dcb1370-aa23-4fb8-98b6-9f378472fa65"
##  [69] "c1cbce80-213f-4e76-8a79-096f1b61865a"
##  [70] "07013054-9c63-4f1b-bbc1-e0088f00869c"
##  [71] "a9112bb1-40bf-445d-9890-0d6347c89d88"
##  [72] "be15d993-13fd-496c-a209-13720b9e4c66"
##  [73] "e443b793-8fd2-4d16-9cdc-7eee8b093dc5"
##  [74] "21568d1d-096e-4eaf-8d3e-3507ec6927e5"
##  [75] "21138f85-b905-452e-8751-f32854c4687f"
##  [76] "247bde9a-028f-4f44-8b3a-dcd036469042"
##  [77] "173deb25-eb90-421f-aaae-36bb1028f83b"
##  [78] "b57c77b6-fe90-4743-a19c-afe2a300b29f"
##  [79] "0e685b11-b20a-4697-94da-969d66d0fca1"
##  [80] "850fb78b-3685-4d17-aabb-60f6cce9e6b8"
##  [81] "c04cddaf-958a-49d4-aea0-1ae12bd1b5ae"
##  [82] "7e5884f0-8501-41bd-a5a6-cda47b731b44"
##  [83] "c21e1445-963d-46f0-b356-2b09baaf5ee3"
##  [84] "8f8c9319-0bd4-4f53-ac63-80f156d3e255"
##  [85] "ff73ae43-1c24-46fa-ab6a-e92e6815e392"
##  [86] "ce523310-dc44-4e76-93ea-b1301186dbeb"
##  [87] "2def5073-227a-461e-bcaa-9ee17b968118"
##  [88] "1e05c12c-15e8-4141-a08f-e2235fbecead"
##  [89] "e93523c3-f7c7-4dfc-a391-6ef68fe92043"
##  [90] "d860e665-d8fe-4a7a-9eef-922d9c0bae8d"
##  [91] "eed1958f-7910-4b0d-b18b-1069eabf72fd"
##  [92] "3416e758-5569-4ee4-a0a1-9b74ce14ae13"
##  [93] "7c415ee8-a947-44ad-a3c0-3d698816e4cd"
##  [94] "63764a2d-c968-4054-9f70-328d0ce63be8"
##  [95] "53eddd04-8641-4588-bd8d-1c559c734214"
##  [96] "712a6a51-545b-434f-8b64-57f43c78b6bd"
##  [97] "21da0257-d661-4cc3-8145-b8fbced6ceaa"
##  [98] "13d8693e-741f-4d36-8b2d-c8858109bfdb"
##  [99] "9349c548-0a7c-4c43-994f-3237a8c0e51a"
## [100] "5ac4a655-d4a9-4dbe-adc1-34f2249a8036"
## [101] "98e38830-04f7-4174-af8d-11f4abfd227d"
## [102] "422e8191-0cff-4f3d-9de4-2f55ea421624"
## [103] "47fd07a2-f786-4402-b65c-844ae8215cd8"
## [104] "400b9db2-cfb6-45fa-96f7-61c54f466e3c"
## [105] "9613d0dc-947a-4126-b685-7d371c921bfa"
## [106] "476ffa1c-0e56-42bf-978c-2cb2897574f6"
## [107] "1f84535b-8b71-4111-b24e-d103d74c6dcf"
## [108] "bf6f19a8-c476-4319-ad7d-ffddaf1f9793"
## [109] "f2ce2535-5fb1-4fab-b92b-dcf9a7140d26"
## [110] "9f230287-b2d1-44b4-9507-3e05a2c6b503"
## [111] "c8101b64-16a4-4d03-b332-0b73fbbb6068"
## [112] "42802e76-08ee-4bca-8daa-5232434d36b2"
## [113] "8f4865b5-fab4-4c8e-91b6-325c6c991a37"
## [114] "d4d8aacf-6e7c-4c37-bae8-1e4fabdff478"
## [115] "f2458ea3-9026-4cca-8e7d-4ee2dbc52e5d"
## [116] "1b33a653-9f48-4964-9b0b-1917ad6f68bb"
## [117] "5e9921d4-d973-4ba5-924b-a204ee581174"
## [118] "fae69a0d-3eb6-416a-9224-25f27d69f8e1"
## [119] "1d5da146-5bb0-45b6-9d12-aca2b8525ee1"
## [120] "90526516-26c5-42f6-b4c0-a99b92871bce"
## [121] "92c5007f-c145-4681-a533-ca6c772293cd"
## [122] "d55e9d0f-38db-4a21-853c-382c863e7c58"
## [123] "88a0c16d-325f-4393-977a-bd3b26709267"
## [124] "d2fc7511-b222-4e48-9acb-57f2e71537e5"
## [125] "5d8331b2-6804-4aeb-a367-961cf4b7fa3c"
## [126] "fce9711d-3bc0-40d2-ae86-618ea0d2aaa8"
## [127] "9e0daef8-6e0d-4f54-8f76-f23512646ce8"
## [128] "704b09b5-56e6-4342-80a0-8a03fbf7b28f"
## [129] "16a54671-9569-42af-ba0d-9b84dc7b129e"
## [130] "e7200334-2f5d-4275-b09a-2c72e4e43714"
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## [494] "2164a8a1-c467-4157-abee-c9bf146004e9"
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## [497] "689d9753-f707-41ee-9d7f-ceaeb1b035d6"
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## [502] "219c0dc7-bba6-4f52-b009-f6cff46c8814"
## [503] "90b1a5e8-f411-4b39-a2f4-4c2c574b02ec"
## [504] "db9e46b7-a6f8-4db7-925b-7efb18b472a3"
## [505] "55003328-8b1e-459c-946b-8faf90504fdc"
## [506] "1d6c1eed-e6ca-477d-8192-4f629dd9d6ba"
## [507] "7b4f0ef6-a9ce-4177-acc5-38f8f93e85f6"
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## [509] "ea0efc32-7c3c-43ab-a007-60656073f9e8"
## [510] "8916c000-9fcc-4d87-b751-f3027b0e2082"
## [511] "963d146f-2960-4b02-9e0a-66cd09ee1605"
## [512] "2d6e53d9-9e17-48f2-97e7-154acdb996b7"
## [513] "a6097554-fca2-4853-b853-b159d9a8f572"
## [514] "3aaf5759-78cb-4596-b154-f9b5a2bb2726"
## [515] "3ea94763-bc87-4097-9cda-c08dafd8a8d5"
## [516] "d4075a1b-b12e-4b2d-9236-e118140be800"
## [517] "84fcf057-7410-4702-831d-2e88b9eaf616"
## [518] "6015e442-444a-487e-abd1-de3809560d3a"
## [519] "9f2d457e-3b18-4832-bfd0-a5cfa73aae44"
## [520] "14d0efdc-f518-4a9d-9b1d-b7dbbccf92ad"
## [521] "47e535fc-3b3a-4acf-974c-3956415bb101"
## [522] "74a1293a-29ab-418e-bded-fcb377b4aa6b"
## [523] "9f974332-a63f-4b58-8bfe-40b941a79e1e"
## [524] "0fa1a18c-2afe-459c-b7d8-c05461c79c8a"
## [525] "e44c6002-f73e-4a1a-a19e-1a0de9cbc742"
## [526] "4ec3713a-2424-490b-a46c-1f8df8ead736"
## [527] "7f73cb5d-6bb4-49e9-92af-8d9ce97bc66b"
## [528] "c8055d9c-94b8-43b4-bd1d-7aad4b97392f"
## [529] "d8450d57-dece-448a-88e1-d32a7d054b2e"
## [530] "e3006c17-4e4c-454b-b38a-2f0ea59a43ef"
## [531] "9e48abb1-3331-48d2-90ef-369091ac3963"
## [532] "26f31e18-1991-40eb-85fc-c9aa406b135b"
## [533] "cb540c5d-54b1-4dc3-add1-8b271c11352e"
## [534] "56351b3c-5897-421d-854f-ea77692b1234"
## [535] "bf6d3850-4cac-4073-b255-7e9ae8e43773"
## [536] "8d47a28e-dd61-4830-8d26-fd1b683d4acc"
## [537] "79a3b463-4766-4521-8b70-524aad54db8b"
## [538] "cf039ad6-7b2c-4732-bdc1-528060d45815"
## [539] "60873cd7-d373-459b-9642-a5dfe91b0fbc"
## [540] "d2376462-b4d6-4d6b-a0e9-d86f93629cf9"
## [541] "6addf87b-c43b-42be-8a76-44efa17bc0a2"
## [542] "d9562416-80e6-4aad-84bb-48c1d0d8f30a"
## [543] "f30fbc5c-4743-483d-857b-71c3162b602d"
## [544] "ce032981-672b-438e-a0b8-ac3ed8cbb98f"
## [545] "ba8077a4-2848-4210-9db6-4681bc043a5d"
## [546] "07a3fca4-5d74-4994-b269-842c5ab5bbaf"
## [547] "29642b15-19bf-47f9-b462-5701ec2f4c31"
## [548] "f75d6632-7114-409f-834b-9b9f8d2a6a39"
## 
## $Encounter.verbatimLocality
##  [1] "praia_grande"     NA                 "anjos"            "forno"           
##  [5] "prainha"          "anequim"          "pontal"           "pedra_vermelha"  
##  [9] "cardeiro"         "porcos_saltador"  "porcos_ponta_sul" "gracainha"       
## [13] "ilha_farol"       "enseada_abobora"  "porcos"           "ponta_leste"     
## [17] "boqueirao"       
## 
## $Encounter.year
##  [1] "2021" "2020" "2019" "2018" "2017" "2016" "2015" "2014" "2013" "2012"
## [11] "2010" "2009" "2008" "2007" "2006"
## 
## $Encounter.month
##  [1] "9"  "8"  "6"  "4"  "3"  "11" "10" "5"  "2"  "1"  "12" "7" 
## 
## $Encounter.day
##  [1] "30" "0"  "17" "18" "26" "25" "5"  "31" "20" "19" "9"  "23" "13" "14" "3" 
## [16] "16" "10" "21" "1"  "29" "15" "12" "11" "4"  "28" "27" "7"  "6"  "2"  "8" 
## [31] "24" "22"
## 
## $Encounter.behavior
## [1] NA         "resting"  "foraging"
## 
## $Encounter.genus
## [1] "Chelonia"     "Eretmochelys"
## 
## $Encounter.specificEpithet
## [1] "mydas"     "imbricata"
## 
## $Encounter.occurrenceRemarks
## [1] "left"  "right" "both" 
## 
## $Encounter.mediaAsset0
##   [1] "744.jpeg" "745.jpeg" "7.jpg"    "741.jpeg" "743.jpeg" "742.jpeg"
##   [7] "740.jpeg" "696.jpeg" "746.jpeg" "747.jpeg" "691.jpg"  "693.jpg" 
##  [13] "690.jpg"  "694.jpg"  "681.jpg"  "682.jpg"  "676.jpg"  "675.jpg" 
##  [19] "687.jpg"  "686.jpg"  "685.jpg"  "678.jpg"  "679.jpg"  "677.jpg" 
##  [25] "674.jpg"  "673.jpg"  "672.jpg"  "669.jpg"  "671.jpg"  "670.jpg" 
##  [31] "668.jpg"  "667.jpg"  "666.jpg"  "664.JPG"  "665.JPG"  "712.JPG" 
##  [37] "716.JPG"  "711.JPG"  "710.JPG"  "713.JPG"  "714.JPG"  "715.JPG" 
##  [43] "680.JPG"  "703.JPG"  "709.JPG"  "708.JPG"  "705.JPG"  "706.JPG" 
##  [49] "704.JPG"  "702.JPG"  "728.JPG"  "721.JPG"  "719.JPG"  "718.JPG" 
##  [55] "737.JPG"  "738.JPG"  "739.JPG"  "736.JPG"  "733.JPG"  "717.JPG" 
##  [61] "720.JPG"  "735.JPG"  "726.JPG"  "722.JPG"  "727.JPG"  "731.JPG" 
##  [67] "723.JPG"  "725.JPG"  "729.JPG"  "689.JPG"  "688.JPG"  "683.JPG" 
##  [73] "684.JPG"  "663.JPG"  "660.JPG"  "700.JPG"  "698.JPG"  "697.JPG" 
##  [79] "661.JPG"  "662.JPG"  "701.JPG"  "655.JPG"  "654.JPG"  "653.JPG" 
##  [85] "656.JPG"  "649.JPG"  "650.JPG"  "651.JPG"  "652.jpg"  "657.JPG" 
##  [91] "647.JPG"  "646.JPG"  "645.JPG"  "644.JPG"  "643.JPG"  "640.JPG" 
##  [97] "639.JPG"  "641.JPG"  "637.jpg"  "635.jpg"  "630.jpeg" "629.jpeg"
## [103] "615.jpeg" "612.jpg"  "610.jpg"  "609.jpg"  "611.jpg"  "613.jpeg"
## [109] "625.jpg"  "626.jpg"  "621.jpg"  "617.jpg"  "619.jpg"  "622.jpg" 
## [115] "620.jpg"  "627.jpg"  "624.jpg"  "623.jpg"  "634.jpg"  "631.jpg" 
## [121] "600.jpg"  "632.jpg"  "582.jpeg" "584.jpg"  "583.jpg"  "597.jpeg"
## [127] "575.jpeg" "580.jpeg" "577.jpeg" "596.jpg"  "492.jpg"  "491.jpg" 
## [133] "495.jpg"  "490.jpg"  "539.jpg"  "489.jpg"  "513.jpg"  "462.jpg" 
## [139] "482.jpg"  "585.jpg"  "481.jpg"  "529.jpeg" "531.jpg"  "511.jpg" 
## [145] "512.jpg"  "537.jpg"  "540.jpg"  "352.JPG"  "365.jpg"  "348.jpeg"
## [151] "338.JPG"  "532.jpg"  "340.JPG"  "339.JPG"  "346.jpeg" "541.jpg" 
## [157] "364.jpg"  "530.jpg"  "376.jpg"  "359.jpg"  "278.jpg"  "363.jpg" 
## [163] "159.jpg"  "266.jpg"  "592.jpg"  "391.jpg"  "240.jpg"  "285.jpg" 
## [169] "173.jpg"  "228.jpg"  "245.jpg"  "608.jpg"  "242.jpg"  "231.jpg" 
## [175] "155.jpg"  "374.jpg"  "293.jpg"  "360.jpg"  "414.jpg"  "460.jpg" 
## [181] "276.jpg"  "312.jpg"  "252.jpg"  "251.jpg"  "141.jpeg" "136.jpeg"
## [187] "151.jpg"  "604.jpg"  "152.jpg"  "153.jpg"  "256.jpg"  "255.jpg" 
## [193] "361.jpg"  "366.jpg"  "595.jpg"  "401.jpg"  "400.jpg"  "399.jpg" 
## [199] "479.jpg"  "275.jpg"  "263.jpg"  "147.jpg"  "154.jpg"  "239.jpg" 
## [205] "477.jpg"  "133.JPG"  "274.jpg"  "594.jpg"  "257.jpg"  "207.jpg" 
## [211] "254.jpg"  "140.jpg"  "134.jpeg" "341.jpg"  "204.jpg"  "132.JPG" 
## [217] "379.jpg"  "235.jpg"  "138.jpg"  "517.jpg"  "343.jpg"  "139.jpg" 
## [223] "543.jpg"  "268.jpg"  "265.jpg"  "264.jpg"  "258.jpg"  "148.jpg" 
## [229] "249.jpg"  "163.jpg"  "237.jpg"  "190.jpg"  "171.jpg"  "412.jpg" 
## [235] "606.jpg"  "162.jpg"  "607.jpg"  "161.jpg"  "322.jpg"  "395.jpg" 
## [241] "357.jpg"  "358.jpg"  "164.jpg"  "565.jpg"  "458.jpg"  "187.jpg" 
## [247] "176.jpg"  "211.jpg"  "601.jpg"  "404.jpg"  "195.jpg"  "405.jpg" 
## [253] "82.JPG"   "91.jpg"   "419.jpg"  "383.jpg"  "84.jpeg"  "202.jpg" 
## [259] "536.jpg"  "402.jpg"  "238.jpg"  "318.jpg"  "381.jpg"  "382.jpg" 
## [265] "350.jpg"  "380.jpg"  "416.jpg"  "415.jpg"  "271.jpg"  "418.jpg" 
## [271] "313.jpg"  "506.jpg"  "542.jpg"  "502.jpg"  "410.jpg"  "297.jpg" 
## [277] "291.jpg"  "406.jpg"  "321.jpg"  "169.jpg"  "325.jpg"  "145.jpg" 
## [283] "306.jpg"  "80.jpeg"  "304.jpg"  "261.jpg"  "501.jpg"  "389.jpg" 
## [289] "290.jpg"  "284.jpg"  "563.jpg"  "199.jpg"  "548.jpg"  "453.jpg" 
## [295] "146.jpg"  "315.jpg"  "553.jpg"  "303.jpg"  "387.jpg"  "279.jpg" 
## [301] "371.jpg"  "250.jpg"  "323.jpg"  "510.jpg"  "61.jpg"   "311.jpg" 
## [307] "378.jpg"  "377.jpg"  "182.jpg"  "179.jpg"  "296.jpg"  "165.jpg" 
## [313] "484.jpg"  "485.jpg"  "487.jpg"  "373.jpg"  "559.jpg"  "175.jpg" 
## [319] "326.jpg"  "76.jpg"   "351.jpg"  "224.jpg"  "508.jpg"  "191.jpg" 
## [325] "638.jpg"  "505.jpg"  "413.jpg"  "347.jpg"  "180.jpg"  "500.jpg" 
## [331] "498.jpg"  "208.jpg"  "388.jpg"  "564.jpg"  "177.jpg"  "496.jpg" 
## [337] "160.jpg"  "198.jpg"  "344.jpg"  "247.jpg"  "558.jpg"  "72.JPG"  
## [343] "277.jpg"  "192.jpg"  "225.jpg"  "349.jpg"  "567.jpg"  "299.jpg" 
## [349] "298.jpg"  "469.jpg"  "214.jpg"  "475.jpg"  "451.jpg"  "470.jpg" 
## [355] "438.jpg"  "218.jpg"  "259.jpg"  "450.jpg"  "178.jpg"  "70.JPG"  
## [361] "557.jpg"  "464.jpg"  "440.jpg"  "69.jpg"   "158.jpeg" "203.jpg" 
## [367] "446.jpg"  "566.jpg"  "262.jpg"  "260.jpg"  "535.jpg"  "456.jpg" 
## [373] "213.jpg"  "283.jpg"  "454.jpg"  "463.jpg"  "447.jpg"  "472.jpg" 
## [379] "183.jpg"  "471.jpg"  "445.jpg"  "217.jpg"  "216.jpg"  "200.jpg" 
## [385] "441.jpg"  "562.jpg"  "466.jpg"  "408.jpg"  "174.jpg"  "633.jpg" 
## [391] "186.jpg"  "307.jpg"  "222.jpg"  "335.jpg"  "221.jpg"  "227.jpg" 
## [397] "63.jpg"   "229.jpg"  "181.jpg"  "356.jpg"  "324.jpg"  "337.jpg" 
## [403] "561.jpg"  "386.jpg"  "129.jpg"  "280.jpg"  "568.jpg"  "572.jpg" 
## [409] "547.jpg"  "309.jpg"  "302.jpg"  "334.jpg"  "571.jpg"  "398.jpg" 
## [415] "555.jpg"  "62.jpg"   "573.jpg"  "320.jpg"  "193.jpg"  "184.jpg" 
## [421] "166.jpg"  "519.jpg"  "59.jpg"   "58.jpg"   "527.jpg"  "57.JPG"  
## [427] "522.jpg"  "189.jpg"  "234.jpg"  "427.jpg"  "556.jpg"  "429.jpg" 
## [433] "423.jpg"  "385.jpg"  "167.jpg"  "435.jpg"  "232.jpg"  "443.jpg" 
## [439] "436.jpg"  "425.jpg"  "422.jpg"  "196.jpg"  "538.jpg"  "420.jpg" 
## [445] "526.jpg"  "448.jpg"  "523.jpg"  "433.jpg"  "520.jpg"  "201.jpg" 
## [451] "220.jpg"  "437.jpg"  "514.jpg"  "528.jpg"  "525.jpg"  "370.jpg" 
## [457] "390.jpg"  "54.jpg"   "233.jpg"  "432.jpg"  "424.jpg"  "421.jpg" 
## [463] "215.jpg"  "586.jpg"  "331.jpg"  "587.jpg"  "210.jpg"  "434.jpg" 
## [469] "431.jpg"  "230.jpg"  "53.jpg"   "549.jpg"  "411.jpg"  "327.jpg" 
## [475] "51.jpg"   "397.jpg"  "544.jpg"  "367.jpg"  "394.jpg"  "409.jpg" 
## [481] "49.jpg"   "50.jpg"   "48.jpg"   "47.JPG"   "43.jpg"   "45.jpg"  
## [487] "44.jpg"   "368.jpg"  "332.jpg"  "476.jpg"  "392.jpg"  "42.jpg"  
## [493] "41.jpg"   "236.jpg"  "112.JPG"  "354.jpg"  "40.jpg"   "111.JPG" 
## [499] "336.jpg"  "126.jpg"  "110.JPG"  "39.jpg"   "38.JPG"   "109.JPG" 
## [505] "106.JPG"  "329.jpg"  "36.jpg"   "333.jpg"  "533.jpg"  "35.jpg"  
## [511] "105.jpg"  "122.jpg"  "123.jpg"  "103.JPG"  "104.JPG"  "31.JPG"  
## [517] "33.JPG"   "30.jpg"   "372.jpg"  "534.jpg"  "28.JPG"   "27.jpg"  
## [523] "25.jpg"   "98.JPG"   "101.JPG"  "102.JPG"  "117.JPG"  "24.JPG"  
## [529] "22.JPG"   "23.JPG"   "20.JPG"   "21.JPG"   "15.JPG"   "12.jpg"  
## [535] "97.jpg"   "11.jpg"   "9.jpg"    "10.jpg"   "8.jpg"    "6.jpg"   
## [541] "4.jpg"    "96.jpg"   "5.jpg"    "328.jpg"  "3.JPG"    "2.JPG"   
## [547] "1.JPG"   
## 
## $Encounter.mediaAsset1
##  [1] NA         "695.jpeg" "707.JPG"  "734.JPG"  "732.JPG"  "724.JPG" 
##  [7] "730.JPG"  "669.jpg"  "636.jpg"  "616.jpeg" "614.jpeg" "628.jpg" 
## [13] "599.jpeg" "598.jpeg" "576.jpeg" "578.jpeg" "494.jpg"  "480.jpg" 
## [19] "345.jpeg" "267.jpg"  "172.jpg"  "384.jpg"  "142.jpeg" "273.jpg" 
## [25] "593.jpg"  "135.jpeg" "342.jpg"  "396.jpg"  "602.jpg"  "92.JPG"  
## [31] "403.jpg"  "272.jpg"  "417.jpg"  "507.jpg"  "503.jpg"  "292.jpg" 
## [37] "168.jpg"  "316.jpg"  "483.jpg"  "486.jpg"  "488.jpg"  "509.jpg" 
## [43] "473.jpg"  "499.jpg"  "497.jpg"  "439.jpg"  "157.jpeg" "185.jpg" 
## [49] "301.jpg"  "426.jpg"  "552.jpg"  "393.jpg"  "99.JPG"

Configurando a data. Em caso de ausência do dia, foi padronizado uso do dia 15

originalenctable$Encounter.day[originalenctable$Encounter.day==0] <- 15
tabledate <- unite(originalenctable, Encounter.year, Encounter.month, Encounter.day, col = "Date", sep = "-")
tabledate$Date <- factor(tabledate$Date)
tabledate$Date <- as.Date(tabledate$Date, format = "%Y-%m-%d")

Definindo qual(is) lateral(is) os indivíduos têm ao longo do banco de dados

tablesides <- tabledate %>%
  filter(!is.na(Name0.value)) %>%
  group_by(Name0.value) %>% 
  mutate(Sides = case_when(any(str_detect(Encounter.occurrenceRemarks,"both")|n_distinct(Encounter.occurrenceRemarks) > 1) ~ 'both',
                           TRUE ~ Encounter.occurrenceRemarks )) %>%
  ungroup

Definindo quais indivíduos já foram revistos

tableresight <- tablesides %>%
  filter(!is.na(Name0.value)) %>%
  group_by(Name0.value) %>% 
  mutate(Resight = case_when (any(n_distinct(Occurrence.occurrenceID) > 1) ~ 'yes',
                           TRUE ~ 'no' ))

Tabela final com o número total de indivíduos

tableturtlesmax <- tableresight %>% 
  filter(!is.na(Name0.value))

Número de fotos que foram avaliadas para criar esse banco de dados

tableturtlesmax %>%
  filter(!is.na(Encounter.mediaAsset0)) %>%
  group_by(Encounter.genus) %>% 
  dplyr::summarise(n_distinct (Encounter.mediaAsset0))
## # A tibble: 2 x 2
##   Encounter.genus `n_distinct(Encounter.mediaAsset0)`
##   <chr>                                         <int>
## 1 Chelonia                                        438
## 2 Eretmochelys                                    109
tableturtlesmax %>%
  filter(!is.na(Encounter.mediaAsset1)) %>%
  group_by(Encounter.genus) %>% 
  dplyr::summarise(n_distinct (Encounter.mediaAsset1))
## # A tibble: 2 x 2
##   Encounter.genus `n_distinct(Encounter.mediaAsset1)`
##   <chr>                                         <int>
## 1 Chelonia                                         41
## 2 Eretmochelys                                     11

Contando o número de indivíduos distintos para cada espécie, de acordo com a(s) lateral(is) que os indivíduos foram registrados.

tableturtlesmax %>%
  filter(!is.na(Name0.value)) %>%
  filter(Encounter.genus == "Chelonia") %>%
  distinct (Name0.value, .keep_all= TRUE) %>% 
  group_by(Sides) %>% 
  dplyr::summarise(count = n())
## # A tibble: 3 x 2
##   Sides count
##   <chr> <int>
## 1 both     30
## 2 left    126
## 3 right   130
tableturtlesmax %>%
  filter(!is.na(Name0.value)) %>%
  filter(Encounter.genus == "Eretmochelys") %>%
  distinct (Name0.value, .keep_all= TRUE) %>% 
  group_by(Sides) %>% 
  dplyr::summarise(count = n())
## # A tibble: 3 x 2
##   Sides count
##   <chr> <int>
## 1 both      9
## 2 left     22
## 3 right    14

Para evitar possível replicação de indivíduos, é preciso selecionar o número mínimo de indivíduos confirmados. Esse número é o valor dos indivíduos identificados que possuem as duas laterais ‘both’ somado com a lateral que mais possui indivíduos identificados. Nesse caso ‘right’ para C. mydas e ‘left’ para E. imbricata.

tableturtlesmin <-tableturtlesmax %>%
  filter(!is.na(Name0.value)) %>%
  filter(Sides %in% c('both', 'right') & Encounter.genus == "Chelonia" | 
         Sides %in% c('both', 'left') & Encounter.genus == "Eretmochelys")

Analisando o número mínimo de indivíduos confirmados

Número de fotos por ano, para cada espécie

tableturtlesmin %>% 
  distinct (Name0.value, .keep_all= TRUE) %>%
  mutate(year = format(Date, "%Y")) %>%
  group_by(year) %>%
ggplot(., aes(x=year, fill=Encounter.genus))+
  geom_bar(width=.8, position = position_dodge2(preserve = "single")) +
  scale_x_discrete(name = "Ano", breaks = seq(2006, 2021, 1)) +
  scale_y_continuous(name = "Fotos", breaks = seq(0, 100, 20),expand = c(0,0,0,1))

Número de Indivíduos por ano, para cada espécie

tableturtlesmin %>% 
  distinct (Name0.value, .keep_all= TRUE) %>%
  mutate(year = format(Date, "%Y")) %>%
  group_by(year) %>%
ggplot(., aes(x=year, fill= Encounter.genus))+
  geom_bar() +
  scale_y_continuous(name = "Indivíduos", breaks = seq(0, 110, 10), expand = c(0,0,0,15))+
  scale_x_discrete(name = "Ano")+
  theme(legend.position = "none")+
  facet_grid(rows = vars(Encounter.genus),scales = "free", space = "free")

Quantidade e frequência de indivíduos que foram revistos

tableturtlesmin %>%
  filter(!is.na(Encounter.mediaAsset0)) %>%
  distinct (Name0.value, .keep_all= TRUE) %>% 
  group_by(Encounter.genus, Resight) %>% 
  dplyr::summarise(count = n()) %>% 
  ungroup() %>% 
  mutate(freq = count / sum(count))
## `summarise()` has grouped output by 'Encounter.genus'. You can override using the `.groups` argument.
## # A tibble: 4 x 4
##   Encounter.genus Resight count   freq
##   <chr>           <chr>   <int>  <dbl>
## 1 Chelonia        no        116 0.607 
## 2 Chelonia        yes        44 0.230 
## 3 Eretmochelys    no         24 0.126 
## 4 Eretmochelys    yes         7 0.0366

Presença dos indivíduos revistos nas fotos

tableturtlesmin %>%
  filter(!is.na(Encounter.mediaAsset0)) %>%
  group_by(Encounter.genus, Resight) %>% 
  dplyr::summarise(n_distinct (Encounter.mediaAsset0))
## `summarise()` has grouped output by 'Encounter.genus'. You can override using the `.groups` argument.
## # A tibble: 4 x 3
## # Groups:   Encounter.genus [2]
##   Encounter.genus Resight `n_distinct(Encounter.mediaAsset0)`
##   <chr>           <chr>                                 <int>
## 1 Chelonia        no                                      116
## 2 Chelonia        yes                                     168
## 3 Eretmochelys    no                                       24
## 4 Eretmochelys    yes                                      65
tableturtlesmin %>%
  filter(!is.na(Encounter.mediaAsset1)) %>%
  group_by(Encounter.genus, Resight) %>% 
  dplyr::summarise(n_distinct (Encounter.mediaAsset1))
## `summarise()` has grouped output by 'Encounter.genus'. You can override using the `.groups` argument.
## # A tibble: 4 x 3
## # Groups:   Encounter.genus [2]
##   Encounter.genus Resight `n_distinct(Encounter.mediaAsset1)`
##   <chr>           <chr>                                 <int>
## 1 Chelonia        no                                        8
## 2 Chelonia        yes                                      32
## 3 Eretmochelys    no                                        4
## 4 Eretmochelys    yes                                       7

Análise dos indivíduos revistos

Histograma

Filtrando apenas os indivíduos que foram revistos

turtlesresighted <- tableturtlesmin %>%
  filter(Resight == "yes")

Identificando a data dos encontros

table_sum <- turtlesresighted %>%
  group_by(Date, Name0.value, Encounter.genus) %>%
  dplyr::summarise(count = n()) %>% 
  mutate_at(vars(count), funs(factor))
## `summarise()` has grouped output by 'Date', 'Name0.value'. You can override using the `.groups` argument.
## Warning: `funs()` was deprecated in dplyr 0.8.0.
## Please use a list of either functions or lambdas: 
## 
##   # Simple named list: 
##   list(mean = mean, median = median)
## 
##   # Auto named with `tibble::lst()`: 
##   tibble::lst(mean, median)
## 
##   # Using lambdas
##   list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

Calculando o intervalo entre o primeiro e último encontro dos indivíduos.

table_range <- table_sum %>%
  group_by(Name0.value, Encounter.genus) %>%
  dplyr::summarise(min = min(Date),
                   max = max(Date)) %>% 
  ungroup() %>% 
  mutate(diff_dias=max-min) %>% 
  mutate(diff_meses=diff_dias/(365.25/12)) %>%
  mutate(diff_anos=diff_dias/(365)) %>%
  mutate_at(vars(diff_meses,diff_anos), round, 1) %>%
  mutate_if(is.difftime,as.numeric)
## `summarise()` has grouped output by 'Name0.value'. You can override using the `.groups` argument.

Calculando descritores do intervalo entre o primeiro e último encontro de cada indivíduo

describeBy(table_range$diff_anos, table_range$Encounter.genus)
## 
##  Descriptive statistics by group 
## group: Chelonia
##    vars  n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 44 1.73 1.52   1.65    1.58 1.85   0 5.4   5.4 0.66    -0.57 0.23
## ------------------------------------------------------------ 
## group: Eretmochelys
##    vars n mean   sd median trimmed  mad min max range skew kurtosis   se
## X1    1 7 2.66 2.07    2.4    2.66 1.78 0.1 5.6   5.5 0.32     -1.7 0.78
median <- ddply(table_range, .(Encounter.genus), summarise, median = median(diff_anos))

Histograma do intervalo entre os encontros com a mediana

ggplot(table_range, aes(x=diff_anos)) + 
  geom_histogram(binwidth= 1, alpha=.7, boundary = 0, aes(fill=Encounter.genus, color=Encounter.genus))+
  scale_y_continuous(name = "Indivíduos",breaks = seq(0, 22, 2), expand = c(0,0,0,2))+
  scale_x_continuous(name = "Intervalo (anos)",breaks = 1:6)+
  geom_vline(data=median, aes(xintercept=median),
             linetype="dashed")+
  facet_grid (rows = vars(Encounter.genus),scales = "free", space = "free")+
  theme(legend.position = "none")

Linha do tempo

Nessa etapa foi escolhido filtrar os indivíduos onde o intervalo entre o primeiro e último encontro é maior do que 12 meses.

table_range <- table_range %>%
  filter ( diff_meses > 12 )

Selecionando os indivíduos de ‘table_range’ em ‘table_sum’

table_sum_ok <- table_sum %>% 
  filter(Name0.value %in% table_range$Name0.value)

Linha do tempo dos indivíduos, marcando todos os encontros

ggplotly (ggplot(table_sum_ok, aes(x = Date, y = Name0.value)) +
  geom_segment(data = table_range, size = 1.6, alpha=.4,
               aes(x = min, xend = max, y = Name0.value, yend = Name0.value))+
  geom_point(aes(color=Encounter.genus), alpha=1, size=2.7) +
  labs(x="Tempo", y="Indivíduos")+
  scale_x_date(date_breaks = "3 months", date_labels = "%m/%Y")+
  theme_light()+
  theme(axis.text.x = element_text(angle = 45, hjust = .9, vjust = .9)))